Overview

Brought to you by YData

Dataset statistics

Number of variables45
Number of observations1914056
Missing cells321292
Missing cells (%)0.4%
Total size in memory657.1 MiB
Average record size in memory360.0 B

Variable types

Numeric32
Text13

Alerts

Penalty has 321292 (16.8%) missing values Missing
Unique ID has unique values Unique
points has 688609 (36.0%) zeros Zeros
with_points has 84261 (4.4%) zeros Zeros
podiums has 644683 (33.7%) zeros Zeros
wins has 851283 (44.5%) zeros Zeros

Reproduction

Analysis started2025-06-14 14:44:48.354284
Analysis finished2025-06-14 14:45:09.638493
Duration21.28 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Unique ID
Real number (ℝ)

Unique 

Distinct1914056
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1367776.899
Minimum0
Maximum2734366
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:09.780475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile137085.75
Q1684734.75
median1368276.5
Q32050815.25
95-th percentile2597796.25
Maximum2734366
Range2734366
Interquartile range (IQR)1366080.5

Descriptive statistics

Standard deviation789062.9573
Coefficient of variation (CV)0.5768944907
Kurtosis-1.198910837
Mean1367776.899
Median Absolute Deviation (MAD)683028
Skewness-0.00116480934
Sum2.618001579 × 1012
Variance6.226203506 × 1011
MonotonicityNot monotonic
2025-06-14T14:45:10.001880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1894944 1
 
< 0.1%
2256345 1
 
< 0.1%
1327539 1
 
< 0.1%
862298 1
 
< 0.1%
154458 1
 
< 0.1%
2647392 1
 
< 0.1%
793817 1
 
< 0.1%
1567396 1
 
< 0.1%
2255547 1
 
< 0.1%
2455902 1
 
< 0.1%
Other values (1914046) 1914046
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
ValueCountFrequency (%)
2734366 1
< 0.1%
2734365 1
< 0.1%
2734364 1
< 0.1%
2734361 1
< 0.1%
2734360 1
< 0.1%

Rider_ID
Real number (ℝ)

Distinct8999
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5509.394095
Minimum1000
Maximum9998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:10.235022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1451
Q13258
median5514
Q37760
95-th percentile9549
Maximum9998
Range8998
Interquartile range (IQR)4502

Descriptive statistics

Standard deviation2597.185731
Coefficient of variation (CV)0.4714104104
Kurtosis-1.200528266
Mean5509.394095
Median Absolute Deviation (MAD)2251
Skewness-0.006407562203
Sum1.054528882 × 1010
Variance6745373.722
MonotonicityNot monotonic
2025-06-14T14:45:10.450411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6549 463
 
< 0.1%
9279 462
 
< 0.1%
8397 455
 
< 0.1%
6583 455
 
< 0.1%
4146 454
 
< 0.1%
1245 444
 
< 0.1%
4390 435
 
< 0.1%
7549 421
 
< 0.1%
3505 417
 
< 0.1%
6897 416
 
< 0.1%
Other values (8989) 1909634
99.8%
ValueCountFrequency (%)
1000 213
< 0.1%
1001 232
< 0.1%
1002 110
< 0.1%
1003 99
< 0.1%
1004 133
< 0.1%
ValueCountFrequency (%)
9998 180
< 0.1%
9997 259
< 0.1%
9996 295
< 0.1%
9995 207
< 0.1%
9994 202
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:10.630407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.334009559
Min length5

Characters and Unicode

Total characters10209593
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMoto2
2nd rowMoto2
3rd rowMoto3
4th rowMoto3
5th rowMotoGP
ValueCountFrequency (%)
moto2 640761
33.5%
motogp 639313
33.4%
moto3 633982
33.1%
2025-06-14T14:45:11.012342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3828112
37.5%
M 1914056
18.7%
t 1914056
18.7%
2 640761
 
6.3%
G 639313
 
6.3%
P 639313
 
6.3%
3 633982
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10209593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3828112
37.5%
M 1914056
18.7%
t 1914056
18.7%
2 640761
 
6.3%
G 639313
 
6.3%
P 639313
 
6.3%
3 633982
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10209593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3828112
37.5%
M 1914056
18.7%
t 1914056
18.7%
2 640761
 
6.3%
G 639313
 
6.3%
P 639313
 
6.3%
3 633982
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10209593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3828112
37.5%
M 1914056
18.7%
t 1914056
18.7%
2 640761
 
6.3%
G 639313
 
6.3%
P 639313
 
6.3%
3 633982
 
6.2%

Circuit_Length_km
Real number (ℝ)

Distinct2401
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.701207506
Minimum3.5
Maximum5.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:11.242032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile3.622
Q14.104
median4.702
Q35.299
95-th percentile5.781
Maximum5.9
Range2.4
Interquartile range (IQR)1.195

Descriptive statistics

Standard deviation0.6910968424
Coefficient of variation (CV)0.1470041136
Kurtosis-1.193077212
Mean4.701207506
Median Absolute Deviation (MAD)0.597
Skewness-6.28901444 × 10-5
Sum8998374.435
Variance0.4776148455
MonotonicityNot monotonic
2025-06-14T14:45:11.475737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.963 1224
 
0.1%
4.6 1204
 
0.1%
4.831 1197
 
0.1%
4.635 1164
 
0.1%
3.599 1153
 
0.1%
5.597 1151
 
0.1%
3.733 1146
 
0.1%
3.977 1142
 
0.1%
3.931 1139
 
0.1%
5.335 1131
 
0.1%
Other values (2391) 1902405
99.4%
ValueCountFrequency (%)
3.5 394
< 0.1%
3.501 777
< 0.1%
3.502 848
< 0.1%
3.503 642
< 0.1%
3.504 652
< 0.1%
ValueCountFrequency (%)
5.9 320
 
< 0.1%
5.899 817
< 0.1%
5.898 827
< 0.1%
5.897 698
< 0.1%
5.896 793
< 0.1%

Laps
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.50189075
Minimum18
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:11.660546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q119
median22
Q324
95-th percentile25
Maximum25
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.293772153
Coefficient of variation (CV)0.1066776955
Kurtosis-1.240769587
Mean21.50189075
Median Absolute Deviation (MAD)2
Skewness-0.00274334205
Sum41155823
Variance5.26139069
MonotonicityNot monotonic
2025-06-14T14:45:11.829108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
24 240752
12.6%
18 240444
12.6%
22 240350
12.6%
25 239582
12.5%
19 238836
12.5%
23 238613
12.5%
20 238509
12.5%
21 236970
12.4%
ValueCountFrequency (%)
18 240444
12.6%
19 238836
12.5%
20 238509
12.5%
21 236970
12.4%
22 240350
12.6%
ValueCountFrequency (%)
25 239582
12.5%
24 240752
12.6%
23 238613
12.5%
22 240350
12.6%
21 236970
12.4%

Grid_Position
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.4981871
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:12.009821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median12
Q317
95-th percentile21
Maximum22
Range21
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.334417722
Coefficient of variation (CV)0.5509057793
Kurtosis-1.202302528
Mean11.4981871
Median Absolute Deviation (MAD)5
Skewness-0.003675126856
Sum22008174
Variance40.12484788
MonotonicityNot monotonic
2025-06-14T14:45:12.192620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
19 89148
 
4.7%
11 88998
 
4.6%
14 88508
 
4.6%
18 88416
 
4.6%
16 88221
 
4.6%
12 87683
 
4.6%
3 87432
 
4.6%
2 87199
 
4.6%
4 86901
 
4.5%
7 86860
 
4.5%
Other values (12) 1034690
54.1%
ValueCountFrequency (%)
1 86796
4.5%
2 87199
4.6%
3 87432
4.6%
4 86901
4.5%
5 86508
4.5%
ValueCountFrequency (%)
22 86097
4.5%
21 85300
4.5%
20 85095
4.4%
19 89148
4.7%
18 88416
4.6%

Avg_Speed_kmh
Real number (ℝ)

Distinct19961
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.6327869
Minimum150
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:12.395632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile160.01
Q1199.35
median249.65
Q3299.46
95-th percentile339.84
Maximum350
Range200
Interquartile range (IQR)100.11

Descriptive statistics

Standard deviation57.73524791
Coefficient of variation (CV)0.2312807089
Kurtosis-1.201671855
Mean249.6327869
Median Absolute Deviation (MAD)50.07
Skewness0.005461447035
Sum477811133.5
Variance3333.358851
MonotonicityNot monotonic
2025-06-14T14:45:12.625098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
262.85 291
 
< 0.1%
213.43 275
 
< 0.1%
165.22 274
 
< 0.1%
240.94 268
 
< 0.1%
277.18 264
 
< 0.1%
246.34 264
 
< 0.1%
269.95 262
 
< 0.1%
295.66 260
 
< 0.1%
187.11 259
 
< 0.1%
179.95 259
 
< 0.1%
Other values (19951) 1911380
99.9%
ValueCountFrequency (%)
150 43
 
< 0.1%
150.01 58
< 0.1%
150.02 113
< 0.1%
150.03 114
< 0.1%
150.04 99
< 0.1%
ValueCountFrequency (%)
350 55
 
< 0.1%
349.99 83
< 0.1%
349.98 132
< 0.1%
349.97 117
< 0.1%
349.96 149
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:12.811582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5742168
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWet
2nd rowWet
3rd rowDry
4th rowWet
5th rowWet
ValueCountFrequency (%)
wet 959552
50.1%
dry 954504
49.9%
2025-06-14T14:45:13.163345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 959552
16.7%
e 959552
16.7%
t 959552
16.7%
D 954504
16.6%
r 954504
16.6%
y 954504
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5742168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 959552
16.7%
e 959552
16.7%
t 959552
16.7%
D 954504
16.6%
r 954504
16.6%
y 954504
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5742168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 959552
16.7%
e 959552
16.7%
t 959552
16.7%
D 954504
16.6%
r 954504
16.6%
y 954504
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5742168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 959552
16.7%
e 959552
16.7%
t 959552
16.7%
D 954504
16.6%
r 954504
16.6%
y 954504
16.6%

Humidity_%
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.51642951
Minimum30
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:13.371227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile32
Q144
median60
Q375
95-th percentile87
Maximum89
Range59
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.33706559
Coefficient of variation (CV)0.2912988183
Kurtosis-1.201504914
Mean59.51642951
Median Absolute Deviation (MAD)15
Skewness-0.002208263399
Sum113917779
Variance300.5738431
MonotonicityNot monotonic
2025-06-14T14:45:13.593998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 33648
 
1.8%
61 33061
 
1.7%
84 33057
 
1.7%
54 32990
 
1.7%
67 32905
 
1.7%
78 32862
 
1.7%
32 32860
 
1.7%
88 32854
 
1.7%
39 32671
 
1.7%
33 32622
 
1.7%
Other values (50) 1584526
82.8%
ValueCountFrequency (%)
30 32019
1.7%
31 31868
1.7%
32 32860
1.7%
33 32622
1.7%
34 30933
1.6%
ValueCountFrequency (%)
89 30803
1.6%
88 32854
1.7%
87 32285
1.7%
86 32283
1.7%
85 31955
1.7%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:13.782069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.668040016
Min length4

Characters and Unicode

Total characters8934890
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHard
2nd rowSoft
3rd rowSoft
4th rowSoft
5th rowHard
ValueCountFrequency (%)
medium 639333
33.4%
soft 638047
33.3%
hard 636676
33.3%
2025-06-14T14:45:14.180745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 1276009
14.3%
M 639333
 
7.2%
e 639333
 
7.2%
i 639333
 
7.2%
u 639333
 
7.2%
m 639333
 
7.2%
S 638047
 
7.1%
o 638047
 
7.1%
f 638047
 
7.1%
t 638047
 
7.1%
Other values (3) 1910028
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8934890
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1276009
14.3%
M 639333
 
7.2%
e 639333
 
7.2%
i 639333
 
7.2%
u 639333
 
7.2%
m 639333
 
7.2%
S 638047
 
7.1%
o 638047
 
7.1%
f 638047
 
7.1%
t 638047
 
7.1%
Other values (3) 1910028
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8934890
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1276009
14.3%
M 639333
 
7.2%
e 639333
 
7.2%
i 639333
 
7.2%
u 639333
 
7.2%
m 639333
 
7.2%
S 638047
 
7.1%
o 638047
 
7.1%
f 638047
 
7.1%
t 638047
 
7.1%
Other values (3) 1910028
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8934890
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1276009
14.3%
M 639333
 
7.2%
e 639333
 
7.2%
i 639333
 
7.2%
u 639333
 
7.2%
m 639333
 
7.2%
S 638047
 
7.1%
o 638047
 
7.1%
f 638047
 
7.1%
t 638047
 
7.1%
Other values (3) 1910028
21.4%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:14.363482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.667289776
Min length4

Characters and Unicode

Total characters8933454
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHard
2nd rowMedium
3rd rowHard
4th rowHard
5th rowMedium
ValueCountFrequency (%)
soft 641825
33.5%
medium 638615
33.4%
hard 633616
33.1%
2025-06-14T14:45:16.720970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 1272231
14.2%
S 641825
 
7.2%
o 641825
 
7.2%
f 641825
 
7.2%
t 641825
 
7.2%
M 638615
 
7.1%
e 638615
 
7.1%
i 638615
 
7.1%
u 638615
 
7.1%
m 638615
 
7.1%
Other values (3) 1900848
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8933454
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1272231
14.2%
S 641825
 
7.2%
o 641825
 
7.2%
f 641825
 
7.2%
t 641825
 
7.2%
M 638615
 
7.1%
e 638615
 
7.1%
i 638615
 
7.1%
u 638615
 
7.1%
m 638615
 
7.1%
Other values (3) 1900848
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8933454
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1272231
14.2%
S 641825
 
7.2%
o 641825
 
7.2%
f 641825
 
7.2%
t 641825
 
7.2%
M 638615
 
7.1%
e 638615
 
7.1%
i 638615
 
7.1%
u 638615
 
7.1%
m 638615
 
7.1%
Other values (3) 1900848
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8933454
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1272231
14.2%
S 641825
 
7.2%
o 641825
 
7.2%
f 641825
 
7.2%
t 641825
 
7.2%
M 638615
 
7.1%
e 638615
 
7.1%
i 638615
 
7.1%
u 638615
 
7.1%
m 638615
 
7.1%
Other values (3) 1900848
21.3%

Penalty
Text

Missing 

Distinct5
Distinct (%)< 0.1%
Missing321292
Missing (%)16.8%
Memory size14.6 MiB
2025-06-14T14:45:16.922319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length3
Mean length4.788671768
Min length3

Characters and Unicode

Total characters7627224
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row+3s
2nd row+5s
3rd rowDNF
4th rowDNS
5th rowDNS
ValueCountFrequency (%)
dns 321908
16.9%
3s 320314
16.8%
dnf 318145
16.7%
ride 316548
16.6%
through 316548
16.6%
5s 315849
16.5%
2025-06-14T14:45:17.303225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 640053
 
8.4%
N 640053
 
8.4%
+ 636163
 
8.3%
s 636163
 
8.3%
h 633096
 
8.3%
S 321908
 
4.2%
3 320314
 
4.2%
F 318145
 
4.2%
g 316548
 
4.2%
u 316548
 
4.2%
Other values (9) 2848233
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7627224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 640053
 
8.4%
N 640053
 
8.4%
+ 636163
 
8.3%
s 636163
 
8.3%
h 633096
 
8.3%
S 321908
 
4.2%
3 320314
 
4.2%
F 318145
 
4.2%
g 316548
 
4.2%
u 316548
 
4.2%
Other values (9) 2848233
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7627224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 640053
 
8.4%
N 640053
 
8.4%
+ 636163
 
8.3%
s 636163
 
8.3%
h 633096
 
8.3%
S 321908
 
4.2%
3 320314
 
4.2%
F 318145
 
4.2%
g 316548
 
4.2%
u 316548
 
4.2%
Other values (9) 2848233
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7627224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 640053
 
8.4%
N 640053
 
8.4%
+ 636163
 
8.3%
s 636163
 
8.3%
h 633096
 
8.3%
S 321908
 
4.2%
3 320314
 
4.2%
F 318145
 
4.2%
g 316548
 
4.2%
u 316548
 
4.2%
Other values (9) 2848233
37.3%

Championship_Points
Real number (ℝ)

Distinct350
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.4614677
Minimum0
Maximum349
Zeros5443
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:17.516869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q187
median174
Q3262
95-th percentile332
Maximum349
Range349
Interquartile range (IQR)175

Descriptive statistics

Standard deviation100.9484764
Coefficient of variation (CV)0.5786290677
Kurtosis-1.195414816
Mean174.4614677
Median Absolute Deviation (MAD)87
Skewness0.001879483758
Sum333929019
Variance10190.59489
MonotonicityNot monotonic
2025-06-14T14:45:17.751370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122 6306
 
0.3%
198 6279
 
0.3%
65 6257
 
0.3%
2 6170
 
0.3%
44 6169
 
0.3%
307 6152
 
0.3%
52 6151
 
0.3%
105 6147
 
0.3%
212 6146
 
0.3%
290 6143
 
0.3%
Other values (340) 1852136
96.8%
ValueCountFrequency (%)
0 5443
0.3%
1 5622
0.3%
2 6170
0.3%
3 5331
0.3%
4 4990
0.3%
ValueCountFrequency (%)
349 5618
0.3%
348 5524
0.3%
347 5935
0.3%
346 6044
0.3%
345 5612
0.3%

Championship_Position
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.54921173
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:17.955585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile23
Maximum24
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.913828067
Coefficient of variation (CV)0.5509372396
Kurtosis-1.199008389
Mean12.54921173
Median Absolute Deviation (MAD)6
Skewness-0.009298555369
Sum24019894
Variance47.80101854
MonotonicityNot monotonic
2025-06-14T14:45:18.173318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15 82110
 
4.3%
7 82033
 
4.3%
16 81591
 
4.3%
23 81325
 
4.2%
12 81254
 
4.2%
17 80907
 
4.2%
22 80479
 
4.2%
24 80276
 
4.2%
13 80142
 
4.2%
21 79738
 
4.2%
Other values (14) 1104201
57.7%
ValueCountFrequency (%)
1 78529
4.1%
2 79736
4.2%
3 77931
4.1%
4 79330
4.1%
5 77942
4.1%
ValueCountFrequency (%)
24 80276
4.2%
23 81325
4.2%
22 80479
4.2%
21 79738
4.2%
20 78685
4.1%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:18.365996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length4.567199183
Min length3

Characters and Unicode

Total characters8741875
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFP3
2nd rowRace
3rd rowRace
4th rowRace
5th rowFP3
ValueCountFrequency (%)
race 275412
14.4%
fp3 274856
14.4%
fp1 274605
14.3%
qualifying 272947
14.3%
fp2 272755
14.3%
fp4 272259
14.2%
sprint 271222
14.2%
2025-06-14T14:45:18.779835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 1094475
 
12.5%
F 1094475
 
12.5%
i 817116
 
9.3%
a 548359
 
6.3%
n 544169
 
6.2%
R 275412
 
3.2%
e 275412
 
3.2%
c 275412
 
3.2%
3 274856
 
3.1%
1 274605
 
3.1%
Other values (12) 3267584
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8741875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 1094475
 
12.5%
F 1094475
 
12.5%
i 817116
 
9.3%
a 548359
 
6.3%
n 544169
 
6.2%
R 275412
 
3.2%
e 275412
 
3.2%
c 275412
 
3.2%
3 274856
 
3.1%
1 274605
 
3.1%
Other values (12) 3267584
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8741875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 1094475
 
12.5%
F 1094475
 
12.5%
i 817116
 
9.3%
a 548359
 
6.3%
n 544169
 
6.2%
R 275412
 
3.2%
e 275412
 
3.2%
c 275412
 
3.2%
3 274856
 
3.1%
1 274605
 
3.1%
Other values (12) 3267584
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8741875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 1094475
 
12.5%
F 1094475
 
12.5%
i 817116
 
9.3%
a 548359
 
6.3%
n 544169
 
6.2%
R 275412
 
3.2%
e 275412
 
3.2%
c 275412
 
3.2%
3 274856
 
3.1%
1 274605
 
3.1%
Other values (12) 3267584
37.4%

year_x
Real number (ℝ)

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1997.912606
Minimum1949
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:19.004577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1949
5-th percentile1965
Q11987
median2001
Q32012
95-th percentile2019
Maximum2021
Range72
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.08565624
Coefficient of variation (CV)0.008551753559
Kurtosis-0.2990686865
Mean1997.912606
Median Absolute Deviation (MAD)12
Skewness-0.6966620803
Sum3824116611
Variance291.9196491
MonotonicityNot monotonic
2025-06-14T14:45:19.231029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019 55805
 
2.9%
2014 55773
 
2.9%
2015 54886
 
2.9%
2013 52767
 
2.8%
2017 52750
 
2.8%
2018 51811
 
2.7%
2012 50821
 
2.7%
2016 50458
 
2.6%
2011 50123
 
2.6%
2010 49898
 
2.6%
Other values (63) 1388964
72.6%
ValueCountFrequency (%)
1949 3605
0.2%
1950 3663
0.2%
1951 4466
0.2%
1952 5424
0.3%
1953 4623
0.2%
ValueCountFrequency (%)
2021 46666
2.4%
2020 43741
2.3%
2019 55805
2.9%
2018 51811
2.7%
2017 52750
2.8%

sequence
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.030907664
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:19.423207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q312
95-th percentile16
Maximum19
Range18
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.699457597
Coefficient of variation (CV)0.5851714144
Kurtosis-0.9089295117
Mean8.030907664
Median Absolute Deviation (MAD)4
Skewness0.3095130002
Sum15371607
Variance22.08490171
MonotonicityNot monotonic
2025-06-14T14:45:19.595016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
6 139937
 
7.3%
5 137678
 
7.2%
3 137521
 
7.2%
7 135797
 
7.1%
2 135751
 
7.1%
4 134981
 
7.1%
1 128662
 
6.7%
8 125239
 
6.5%
9 119495
 
6.2%
10 119086
 
6.2%
Other values (9) 599909
31.3%
ValueCountFrequency (%)
1 128662
6.7%
2 135751
7.1%
3 137521
7.2%
4 134981
7.1%
5 137678
7.2%
ValueCountFrequency (%)
19 6247
 
0.3%
18 35668
1.9%
17 41931
2.2%
16 57715
3.0%
15 74170
3.9%

rider
Real number (ℝ)

Distinct2695
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1661.072926
Minimum1
Maximum2704
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:19.803746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile288
Q11179
median1779
Q32224
95-th percentile2574
Maximum2704
Range2703
Interquartile range (IQR)1045

Descriptive statistics

Standard deviation703.4290604
Coefficient of variation (CV)0.4234787344
Kurtosis-0.6725323936
Mean1661.072926
Median Absolute Deviation (MAD)523
Skewness-0.5537516022
Sum3179386600
Variance494812.443
MonotonicityNot monotonic
2025-06-14T14:45:20.021919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1762 13950
 
0.7%
2055 10888
 
0.6%
2075 10335
 
0.5%
2064 9884
 
0.5%
2015 9775
 
0.5%
1516 9655
 
0.5%
2058 9623
 
0.5%
2152 9419
 
0.5%
2059 9163
 
0.5%
2052 9005
 
0.5%
Other values (2685) 1812359
94.7%
ValueCountFrequency (%)
1 800
< 0.1%
2 676
< 0.1%
3 228
 
< 0.1%
4 754
< 0.1%
5 311
 
< 0.1%
ValueCountFrequency (%)
2704 40
< 0.1%
2703 29
< 0.1%
2702 35
< 0.1%
2701 33
< 0.1%
2700 57
< 0.1%

team
Real number (ℝ)

Distinct970
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.2452875
Minimum1
Maximum970
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:20.238232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q3495
95-th percentile814
Maximum970
Range969
Interquartile range (IQR)494

Descriptive statistics

Standard deviation300.140805
Coefficient of variation (CV)1.407490916
Kurtosis-0.5674052028
Mean213.2452875
Median Absolute Deviation (MAD)0
Skewness1.008603844
Sum408163422
Variance90084.50282
MonotonicityNot monotonic
2025-06-14T14:45:20.463546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1071637
56.0%
6 20277
 
1.1%
568 18131
 
0.9%
722 13795
 
0.7%
81 13376
 
0.7%
344 11948
 
0.6%
497 11943
 
0.6%
569 10684
 
0.6%
716 10421
 
0.5%
615 9797
 
0.5%
Other values (960) 722047
37.7%
ValueCountFrequency (%)
1 1071637
56.0%
2 938
 
< 0.1%
3 1097
 
0.1%
4 1026
 
0.1%
5 4621
 
0.2%
ValueCountFrequency (%)
970 66
 
< 0.1%
969 36
 
< 0.1%
968 216
< 0.1%
967 31
 
< 0.1%
966 223
< 0.1%

bike
Real number (ℝ)

Distinct301
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.20552429
Minimum1
Maximum304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:20.671369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median7
Q372
95-th percentile258
Maximum304
Range303
Interquartile range (IQR)69

Descriptive statistics

Standard deviation97.37775366
Coefficient of variation (CV)1.565419708
Kurtosis0.003544812644
Mean62.20552429
Median Absolute Deviation (MAD)5
Skewness1.328219133
Sum119064857
Variance9482.426908
MonotonicityNot monotonic
2025-06-14T14:45:20.894174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 453995
23.7%
7 264759
13.8%
4 248535
13.0%
258 109061
 
5.7%
6 104504
 
5.5%
5 80406
 
4.2%
3 57823
 
3.0%
243 36146
 
1.9%
62 27936
 
1.5%
149 27197
 
1.4%
Other values (291) 503694
26.3%
ValueCountFrequency (%)
1 36
 
< 0.1%
2 453995
23.7%
3 57823
 
3.0%
4 248535
13.0%
5 80406
 
4.2%
ValueCountFrequency (%)
304 1067
 
0.1%
303 2180
 
0.1%
302 8989
0.5%
301 36
 
< 0.1%
300 26
 
< 0.1%

position
Real number (ℝ)

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.034194402
Minimum-5
Maximum40
Zeros0
Zeros (%)0.0%
Negative225159
Negative (%)11.8%
Memory size14.6 MiB
2025-06-14T14:45:21.108898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile-1
Q13
median8
Q314
95-th percentile23
Maximum40
Range45
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.654216563
Coefficient of variation (CV)0.8472494859
Kurtosis-0.4549351863
Mean9.034194402
Median Absolute Deviation (MAD)6
Skewness0.5461640168
Sum17291954
Variance58.58703119
MonotonicityNot monotonic
2025-06-14T14:45:21.318485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
-1 190416
 
9.9%
1 104412
 
5.5%
3 104072
 
5.4%
5 103963
 
5.4%
2 103939
 
5.4%
6 103647
 
5.4%
4 103596
 
5.4%
8 85732
 
4.5%
7 85391
 
4.5%
9 85098
 
4.4%
Other values (33) 843790
44.1%
ValueCountFrequency (%)
-5 2059
 
0.1%
-4 24595
 
1.3%
-3 1324
 
0.1%
-2 6765
 
0.4%
-1 190416
9.9%
ValueCountFrequency (%)
40 43
 
< 0.1%
37 35
 
< 0.1%
36 37
 
< 0.1%
35 104
 
< 0.1%
34 354
< 0.1%

points
Real number (ℝ)

Zeros 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.237160773
Minimum0
Maximum25
Zeros688609
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:21.516596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q38
95-th percentile20
Maximum25
Range25
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.197082721
Coefficient of variation (CV)1.183290525
Kurtosis1.27984277
Mean5.237160773
Median Absolute Deviation (MAD)3
Skewness1.329237533
Sum10024219
Variance38.40383426
MonotonicityNot monotonic
2025-06-14T14:45:21.712268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 688609
36.0%
8 104305
 
5.4%
6 104066
 
5.4%
4 103145
 
5.4%
3 102594
 
5.4%
2 101239
 
5.3%
1 99230
 
5.2%
10 84827
 
4.4%
5 83562
 
4.4%
20 55525
 
2.9%
Other values (17) 386954
20.2%
ValueCountFrequency (%)
0 688609
36.0%
0.5 31
 
< 0.1%
1 99230
 
5.2%
1.5 32
 
< 0.1%
2 101239
 
5.3%
ValueCountFrequency (%)
25 48565
2.5%
20 55525
2.9%
17 6021
 
0.3%
16 48341
2.5%
15 36141
1.9%
Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:21.970791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.032510543
Min length2

Characters and Unicode

Total characters5804395
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEMI
2nd rowNAT
3rd rowSPA
4th rowAUS
5th rowCAT
ValueCountFrequency (%)
ned 142576
 
7.4%
spa 119869
 
6.3%
fra 118378
 
6.2%
gbr 99697
 
5.2%
jpn 83811
 
4.4%
aus 79665
 
4.2%
ger 78577
 
4.1%
ita 76682
 
4.0%
cze 75275
 
3.9%
cat 72092
 
3.8%
Other values (43) 967434
50.5%
2025-06-14T14:45:22.452525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 965855
16.6%
R 611470
10.5%
E 513192
 
8.8%
T 449642
 
7.7%
S 365136
 
6.3%
N 358578
 
6.2%
G 312044
 
5.4%
P 277252
 
4.8%
U 236494
 
4.1%
C 198753
 
3.4%
Other values (14) 1515979
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5804395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 965855
16.6%
R 611470
10.5%
E 513192
 
8.8%
T 449642
 
7.7%
S 365136
 
6.3%
N 358578
 
6.2%
G 312044
 
5.4%
P 277252
 
4.8%
U 236494
 
4.1%
C 198753
 
3.4%
Other values (14) 1515979
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5804395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 965855
16.6%
R 611470
10.5%
E 513192
 
8.8%
T 449642
 
7.7%
S 365136
 
6.3%
N 358578
 
6.2%
G 312044
 
5.4%
P 277252
 
4.8%
U 236494
 
4.1%
C 198753
 
3.4%
Other values (14) 1515979
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5804395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 965855
16.6%
R 611470
10.5%
E 513192
 
8.8%
T 449642
 
7.7%
S 365136
 
6.3%
N 358578
 
6.2%
G 312044
 
5.4%
P 277252
 
4.8%
U 236494
 
4.1%
C 198753
 
3.4%
Other values (14) 1515979
26.1%
Distinct70
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:22.788515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length8.3300149
Min length4

Characters and Unicode

Total characters15944115
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMisano
2nd rowImola
3rd rowJarama
4th rowPhillip Island
5th rowCatalunya
ValueCountFrequency (%)
assen 142576
 
6.0%
brno 102898
 
4.3%
jerez 95269
 
4.0%
mugello 85590
 
3.6%
le 84352
 
3.5%
mans 84352
 
3.5%
catalunya 79338
 
3.3%
sachsenring 76297
 
3.2%
misano 75686
 
3.2%
phillip 64001
 
2.7%
Other values (77) 1501056
62.8%
2025-06-14T14:45:23.321021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1666662
 
10.5%
n 1427171
 
9.0%
e 1228620
 
7.7%
r 1124666
 
7.1%
o 1094069
 
6.9%
s 970015
 
6.1%
i 896267
 
5.6%
l 850826
 
5.3%
g 588238
 
3.7%
477359
 
3.0%
Other values (42) 5620222
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15944115
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1666662
 
10.5%
n 1427171
 
9.0%
e 1228620
 
7.7%
r 1124666
 
7.1%
o 1094069
 
6.9%
s 970015
 
6.1%
i 896267
 
5.6%
l 850826
 
5.3%
g 588238
 
3.7%
477359
 
3.0%
Other values (42) 5620222
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15944115
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1666662
 
10.5%
n 1427171
 
9.0%
e 1228620
 
7.7%
r 1124666
 
7.1%
o 1094069
 
6.9%
s 970015
 
6.1%
i 896267
 
5.6%
l 850826
 
5.3%
g 588238
 
3.7%
477359
 
3.0%
Other values (42) 5620222
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15944115
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1666662
 
10.5%
n 1427171
 
9.0%
e 1228620
 
7.7%
r 1124666
 
7.1%
o 1094069
 
6.9%
s 970015
 
6.1%
i 896267
 
5.6%
l 850826
 
5.3%
g 588238
 
3.7%
477359
 
3.0%
Other values (42) 5620222
35.2%
Distinct2695
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:23.709568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length14.47578127
Min length7

Characters and Unicode

Total characters27707456
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOgura, Ai
2nd rowNorth, Alan
3rd rowBiliotti, Fabio
4th rowLocatelli, Roberto
5th rowBinder, Brad
ValueCountFrequency (%)
alex 46829
 
1.2%
andrea 30718
 
0.8%
de 27227
 
0.7%
lorenzo 25126
 
0.6%
jorge 23271
 
0.6%
john 23013
 
0.6%
marco 21356
 
0.5%
jean 21203
 
0.5%
rossi 20750
 
0.5%
espargaro 18422
 
0.5%
Other values (3461) 3685425
93.5%
2025-06-14T14:45:24.618264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2515467
 
9.1%
e 2102881
 
7.6%
i 2077645
 
7.5%
2029284
 
7.3%
, 1914056
 
6.9%
o 1843160
 
6.7%
r 1758341
 
6.3%
n 1664765
 
6.0%
l 1102560
 
4.0%
s 928035
 
3.3%
Other values (59) 9771262
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27707456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2515467
 
9.1%
e 2102881
 
7.6%
i 2077645
 
7.5%
2029284
 
7.3%
, 1914056
 
6.9%
o 1843160
 
6.7%
r 1758341
 
6.3%
n 1664765
 
6.0%
l 1102560
 
4.0%
s 928035
 
3.3%
Other values (59) 9771262
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27707456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2515467
 
9.1%
e 2102881
 
7.6%
i 2077645
 
7.5%
2029284
 
7.3%
, 1914056
 
6.9%
o 1843160
 
6.7%
r 1758341
 
6.3%
n 1664765
 
6.0%
l 1102560
 
4.0%
s 928035
 
3.3%
Other values (59) 9771262
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27707456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2515467
 
9.1%
e 2102881
 
7.6%
i 2077645
 
7.5%
2029284
 
7.3%
, 1914056
 
6.9%
o 1843160
 
6.7%
r 1758341
 
6.3%
n 1664765
 
6.0%
l 1102560
 
4.0%
s 928035
 
3.3%
Other values (59) 9771262
35.3%
Distinct967
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:24.982327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length1
Mean length8.599688306
Min length1

Characters and Unicode

Total characters16460285
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIDEMITSU Honda Team Asia
2nd row?
3rd row?
4th rowMetis Gilera
5th rowRed Bull KTM Ajo
ValueCountFrequency (%)
1105997
30.1%
team 275304
 
7.5%
racing 260728
 
7.1%
honda 82100
 
2.2%
ktm 46170
 
1.3%
red 42774
 
1.2%
bull 42774
 
1.2%
aspar 41694
 
1.1%
yamaha 41521
 
1.1%
gresini 38892
 
1.1%
Other values (896) 1697120
46.2%
2025-06-14T14:45:25.586177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1762237
 
10.7%
a 1663467
 
10.1%
? 1072294
 
6.5%
e 994206
 
6.0%
i 862769
 
5.2%
n 808575
 
4.9%
o 738381
 
4.5%
r 662717
 
4.0%
t 560243
 
3.4%
c 547925
 
3.3%
Other values (71) 6787471
41.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16460285
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1762237
 
10.7%
a 1663467
 
10.1%
? 1072294
 
6.5%
e 994206
 
6.0%
i 862769
 
5.2%
n 808575
 
4.9%
o 738381
 
4.5%
r 662717
 
4.0%
t 560243
 
3.4%
c 547925
 
3.3%
Other values (71) 6787471
41.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16460285
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1762237
 
10.7%
a 1663467
 
10.1%
? 1072294
 
6.5%
e 994206
 
6.0%
i 862769
 
5.2%
n 808575
 
4.9%
o 738381
 
4.5%
r 662717
 
4.0%
t 560243
 
3.4%
c 547925
 
3.3%
Other values (71) 6787471
41.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16460285
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1762237
 
10.7%
a 1663467
 
10.1%
? 1072294
 
6.5%
e 994206
 
6.0%
i 862769
 
5.2%
n 808575
 
4.9%
o 738381
 
4.5%
r 662717
 
4.0%
t 560243
 
3.4%
c 547925
 
3.3%
Other values (71) 6787471
41.2%
Distinct301
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:26.036162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length5.938642861
Min length1

Characters and Unicode

Total characters11366895
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKalex
2nd rowYamaha
3rd rowHonda
4th rowGilera
5th rowKTM
ValueCountFrequency (%)
honda 475538
23.0%
yamaha 276824
13.4%
aprilia 264759
12.8%
kalex 122306
 
5.9%
ktm 115615
 
5.6%
suzuki 80447
 
3.9%
ducati 57823
 
2.8%
suter 40350
 
2.0%
derbi 27936
 
1.4%
mba 27197
 
1.3%
Other values (286) 578250
28.0%
2025-06-14T14:45:26.702785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2090037
18.4%
i 926970
 
8.2%
o 647829
 
5.7%
n 586539
 
5.2%
d 567648
 
5.0%
r 557524
 
4.9%
l 515548
 
4.5%
H 493763
 
4.3%
e 432838
 
3.8%
A 342976
 
3.0%
Other values (55) 4205223
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11366895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2090037
18.4%
i 926970
 
8.2%
o 647829
 
5.7%
n 586539
 
5.2%
d 567648
 
5.0%
r 557524
 
4.9%
l 515548
 
4.5%
H 493763
 
4.3%
e 432838
 
3.8%
A 342976
 
3.0%
Other values (55) 4205223
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11366895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2090037
18.4%
i 926970
 
8.2%
o 647829
 
5.7%
n 586539
 
5.2%
d 567648
 
5.0%
r 557524
 
4.9%
l 515548
 
4.5%
H 493763
 
4.3%
e 432838
 
3.8%
A 342976
 
3.0%
Other values (55) 4205223
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11366895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2090037
18.4%
i 926970
 
8.2%
o 647829
 
5.7%
n 586539
 
5.2%
d 567648
 
5.0%
r 557524
 
4.9%
l 515548
 
4.5%
H 493763
 
4.3%
e 432838
 
3.8%
A 342976
 
3.0%
Other values (55) 4205223
37.0%

Lap_Time_Seconds
Real number (ℝ)

Distinct30205
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.00211362
Minimum70.001
Maximum109.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:26.937588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70.001
5-th percentile72.001
Q180.013
median89.981
Q399.917
95-th percentile108.028
Maximum109.999
Range39.998
Interquartile range (IQR)19.904

Descriptive statistics

Standard deviation11.53121218
Coefficient of variation (CV)0.1281215709
Kurtosis-1.195505716
Mean90.00211362
Median Absolute Deviation (MAD)9.951
Skewness0.002824528105
Sum172269085.6
Variance132.9688544
MonotonicityNot monotonic
2025-06-14T14:45:27.163457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106.857 318
 
< 0.1%
75.885 306
 
< 0.1%
77.268 293
 
< 0.1%
81.366 286
 
< 0.1%
88.304 277
 
< 0.1%
99.313 272
 
< 0.1%
99.143 270
 
< 0.1%
86.325 266
 
< 0.1%
97.656 250
 
< 0.1%
98.232 250
 
< 0.1%
Other values (30195) 1911268
99.9%
ValueCountFrequency (%)
70.001 112
< 0.1%
70.002 32
 
< 0.1%
70.003 35
 
< 0.1%
70.006 48
< 0.1%
70.007 31
 
< 0.1%
ValueCountFrequency (%)
109.999 47
< 0.1%
109.998 35
< 0.1%
109.997 84
< 0.1%
109.996 32
 
< 0.1%
109.995 79
< 0.1%

Corners_per_Lap
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.49956166
Minimum10
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:27.367562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q113
median18
Q322
95-th percentile25
Maximum25
Range15
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.607236719
Coefficient of variation (CV)0.2632772641
Kurtosis-1.210603906
Mean17.49956166
Median Absolute Deviation (MAD)4
Skewness-0.00189825398
Sum33495141
Variance21.22663018
MonotonicityNot monotonic
2025-06-14T14:45:27.581326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
13 123400
 
6.4%
17 122793
 
6.4%
22 121185
 
6.3%
19 120767
 
6.3%
24 120535
 
6.3%
12 119827
 
6.3%
23 119796
 
6.3%
10 119795
 
6.3%
20 119792
 
6.3%
14 119435
 
6.2%
Other values (6) 706731
36.9%
ValueCountFrequency (%)
10 119795
6.3%
11 118149
6.2%
12 119827
6.3%
13 123400
6.4%
14 119435
6.2%
ValueCountFrequency (%)
25 117697
6.1%
24 120535
6.3%
23 119796
6.3%
22 121185
6.3%
21 118460
6.2%
Distinct46
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002751516309
Minimum0.0005
Maximum0.005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:27.807833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.0007
Q10.0016
median0.0028
Q30.0039
95-th percentile0.0048
Maximum0.005
Range0.0045
Interquartile range (IQR)0.0023

Descriptive statistics

Standard deviation0.001299112231
Coefficient of variation (CV)0.4721441145
Kurtosis-1.194564175
Mean0.002751516309
Median Absolute Deviation (MAD)0.0011
Skewness-0.005944567449
Sum5266.5563
Variance1.687692589 × 10-6
MonotonicityNot monotonic
2025-06-14T14:45:28.038080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0044 45600
 
2.4%
0.0022 44672
 
2.3%
0.0036 44587
 
2.3%
0.0031 44189
 
2.3%
0.0041 44031
 
2.3%
0.0006 43996
 
2.3%
0.0033 43982
 
2.3%
0.0035 43825
 
2.3%
0.0014 43488
 
2.3%
0.0016 43308
 
2.3%
Other values (36) 1472378
76.9%
ValueCountFrequency (%)
0.0005 21375
1.1%
0.0006 43996
2.3%
0.0007 42887
2.2%
0.0008 41725
2.2%
0.0009 41876
2.2%
ValueCountFrequency (%)
0.005 20665
1.1%
0.0049 42879
2.2%
0.0048 42627
2.2%
0.0047 42036
2.2%
0.0046 42356
2.2%

Pit_Stop_Duration_Seconds
Real number (ℝ)

Distinct301
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.501716371
Minimum2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:28.287759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.15
Q12.75
median3.5
Q34.25
95-th percentile4.85
Maximum5
Range3
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.8681806347
Coefficient of variation (CV)0.2479300271
Kurtosis-1.205288469
Mean3.501716371
Median Absolute Deviation (MAD)0.75
Skewness0.0002063209519
Sum6702481.23
Variance0.7537376145
MonotonicityNot monotonic
2025-06-14T14:45:28.510131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.69 7701
 
0.4%
2.04 7568
 
0.4%
3.79 7497
 
0.4%
4.03 7450
 
0.4%
3.83 7437
 
0.4%
3.88 7370
 
0.4%
4.97 7329
 
0.4%
4.92 7328
 
0.4%
3.76 7294
 
0.4%
2.82 7262
 
0.4%
Other values (291) 1839820
96.1%
ValueCountFrequency (%)
2 3286
0.2%
2.01 5804
0.3%
2.02 5915
0.3%
2.03 6403
0.3%
2.04 7568
0.4%
ValueCountFrequency (%)
5 3120
0.2%
4.99 6516
0.3%
4.98 6683
0.3%
4.97 7329
0.4%
4.96 5724
0.3%
Distinct201
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.98259058
Minimum15
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:28.751388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile16.1
Q120
median24.9
Q330
95-th percentile34
Maximum35
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.761290958
Coefficient of variation (CV)0.2306122313
Kurtosis-1.203821056
Mean24.98259058
Median Absolute Deviation (MAD)5
Skewness0.00415982352
Sum47818077.4
Variance33.1924735
MonotonicityNot monotonic
2025-06-14T14:45:28.992973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.4 10966
 
0.6%
28.6 10767
 
0.6%
27.2 10759
 
0.6%
34.2 10688
 
0.6%
33.7 10666
 
0.6%
20.6 10599
 
0.6%
29.2 10589
 
0.6%
23.8 10554
 
0.6%
18.4 10527
 
0.5%
17.3 10524
 
0.5%
Other values (191) 1807417
94.4%
ValueCountFrequency (%)
15 4143
0.2%
15.1 9082
0.5%
15.2 8566
0.4%
15.3 9231
0.5%
15.4 9619
0.5%
ValueCountFrequency (%)
35 4654
0.2%
34.9 9128
0.5%
34.8 8989
0.5%
34.7 8823
0.5%
34.6 9161
0.5%

Track_Temperature_Celsius
Real number (ℝ)

Distinct350
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.49059129
Minimum15.1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:29.220409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.1
5-th percentile20.5
Q127.2
median32.4
Q337.7
95-th percentile44.6
Maximum50
Range34.9
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation7.215788696
Coefficient of variation (CV)0.2220885619
Kurtosis-0.652533586
Mean32.49059129
Median Absolute Deviation (MAD)5.3
Skewness0.01074700171
Sum62188811.2
Variance52.0676065
MonotonicityNot monotonic
2025-06-14T14:45:29.462008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.8 11072
 
0.6%
30.3 10650
 
0.6%
30.1 10358
 
0.5%
31.1 10323
 
0.5%
31.9 10297
 
0.5%
30.5 10227
 
0.5%
32.4 10225
 
0.5%
34.9 10209
 
0.5%
35.7 10191
 
0.5%
30.9 10153
 
0.5%
Other values (340) 1810351
94.6%
ValueCountFrequency (%)
15.1 94
 
< 0.1%
15.2 76
 
< 0.1%
15.3 153
< 0.1%
15.4 265
< 0.1%
15.5 282
< 0.1%
ValueCountFrequency (%)
50 33
 
< 0.1%
49.9 30
 
< 0.1%
49.8 208
< 0.1%
49.7 215
< 0.1%
49.6 248
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:29.651610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length7
Mean length6.998230459
Min length5

Characters and Unicode

Total characters13395005
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClear
2nd rowRaining
3rd rowCloudy
4th rowClear
5th rowCloudy
ValueCountFrequency (%)
cloudy 1041448
45.8%
sunny 679912
29.9%
partly 359045
 
15.8%
raining 134981
 
5.9%
clear 57715
 
2.5%
2025-06-14T14:45:30.056361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
y 2080405
15.5%
u 1721360
12.9%
n 1629786
12.2%
l 1458208
10.9%
o 1041448
7.8%
d 1041448
7.8%
C 740118
 
5.5%
S 679912
 
5.1%
a 551741
 
4.1%
r 416760
 
3.1%
Other values (8) 2033819
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13395005
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
y 2080405
15.5%
u 1721360
12.9%
n 1629786
12.2%
l 1458208
10.9%
o 1041448
7.8%
d 1041448
7.8%
C 740118
 
5.5%
S 679912
 
5.1%
a 551741
 
4.1%
r 416760
 
3.1%
Other values (8) 2033819
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13395005
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
y 2080405
15.5%
u 1721360
12.9%
n 1629786
12.2%
l 1458208
10.9%
o 1041448
7.8%
d 1041448
7.8%
C 740118
 
5.5%
S 679912
 
5.1%
a 551741
 
4.1%
r 416760
 
3.1%
Other values (8) 2033819
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13395005
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
y 2080405
15.5%
u 1721360
12.9%
n 1629786
12.2%
l 1458208
10.9%
o 1041448
7.8%
d 1041448
7.8%
C 740118
 
5.5%
S 679912
 
5.1%
a 551741
 
4.1%
r 416760
 
3.1%
Other values (8) 2033819
15.2%

track
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:30.203913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5742168
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDry
2nd rowWet
3rd rowDry
4th rowDry
5th rowDry
ValueCountFrequency (%)
dry 1659989
86.7%
wet 254067
 
13.3%
2025-06-14T14:45:30.505617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 1659989
28.9%
r 1659989
28.9%
y 1659989
28.9%
W 254067
 
4.4%
e 254067
 
4.4%
t 254067
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5742168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 1659989
28.9%
r 1659989
28.9%
y 1659989
28.9%
W 254067
 
4.4%
e 254067
 
4.4%
t 254067
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5742168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 1659989
28.9%
r 1659989
28.9%
y 1659989
28.9%
W 254067
 
4.4%
e 254067
 
4.4%
t 254067
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5742168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 1659989
28.9%
r 1659989
28.9%
y 1659989
28.9%
W 254067
 
4.4%
e 254067
 
4.4%
t 254067
 
4.4%

air
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.90190099
Minimum12
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:30.679191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12
Q117
median21
Q326
95-th percentile36
Maximum36
Range24
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.928274893
Coefficient of variation (CV)0.2706739883
Kurtosis0.1194876405
Mean21.90190099
Median Absolute Deviation (MAD)5
Skewness0.448513549
Sum41921465
Variance35.14444321
MonotonicityNot monotonic
2025-06-14T14:45:30.845235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
21 349282
18.2%
22 271548
14.2%
26 246816
12.9%
16 139937
7.3%
12 134981
 
7.1%
28 128662
 
6.7%
36 126601
 
6.6%
27 125239
 
6.5%
14 119086
 
6.2%
17 98104
 
5.1%
Other values (4) 173800
9.1%
ValueCountFrequency (%)
12 134981
7.1%
13 6247
 
0.3%
14 119086
6.2%
16 139937
7.3%
17 98104
5.1%
ValueCountFrequency (%)
36 126601
6.6%
28 128662
6.7%
27 125239
6.5%
26 246816
12.9%
23 57715
 
3.0%

ground
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.73456628
Minimum12
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:31.018981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12
Q121
median29
Q340
95-th percentile48
Maximum54
Range42
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.62753255
Coefficient of variation (CV)0.3910442963
Kurtosis-0.9967131339
Mean29.73456628
Median Absolute Deviation (MAD)8
Skewness0.2955360938
Sum56913625
Variance135.1995131
MonotonicityNot monotonic
2025-06-14T14:45:31.199604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
31 228063
11.9%
23 209921
11.0%
35 166853
8.7%
19 139937
 
7.3%
40 137678
 
7.2%
21 137521
 
7.2%
12 134981
 
7.1%
47 128662
 
6.7%
46 125239
 
6.5%
29 119495
 
6.2%
Other values (6) 385706
20.2%
ValueCountFrequency (%)
12 134981
7.1%
14 119086
6.2%
15 6247
 
0.3%
19 139937
7.3%
21 137521
7.2%
ValueCountFrequency (%)
54 41931
 
2.2%
48 84670
4.4%
47 128662
6.7%
46 125239
6.5%
40 137678
7.2%

starts
Real number (ℝ)

Distinct188
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.2372303
Minimum1
Maximum406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:31.400543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q135
median85
Q3146
95-th percentile261
Maximum406
Range405
Interquartile range (IQR)111

Descriptive statistics

Standard deviation81.43523676
Coefficient of variation (CV)0.8044000863
Kurtosis0.7147019727
Mean101.2372303
Median Absolute Deviation (MAD)53
Skewness1.00614546
Sum193773728
Variance6631.697786
MonotonicityNot monotonic
2025-06-14T14:45:31.626256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 27605
 
1.4%
146 24705
 
1.3%
102 24211
 
1.3%
220 22511
 
1.2%
2 22213
 
1.2%
54 22041
 
1.2%
106 21707
 
1.1%
90 21433
 
1.1%
3 18763
 
1.0%
77 18272
 
1.0%
Other values (178) 1690595
88.3%
ValueCountFrequency (%)
1 27605
1.4%
2 22213
1.2%
3 18763
1.0%
4 16947
0.9%
5 14982
0.8%
ValueCountFrequency (%)
406 13950
0.7%
320 10888
0.6%
307 10335
0.5%
294 9884
0.5%
290 9775
0.5%

finishes
Real number (ℝ)

Distinct174
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.46166936
Minimum0
Maximum373
Zeros3057
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:31.847745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q131
median74
Q3123
95-th percentile216
Maximum373
Range373
Interquartile range (IQR)92

Descriptive statistics

Standard deviation69.86619633
Coefficient of variation (CV)0.7988207502
Kurtosis1.364094895
Mean87.46166936
Median Absolute Deviation (MAD)45
Skewness1.115281039
Sum167406533
Variance4881.285389
MonotonicityNot monotonic
2025-06-14T14:45:32.069614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 29394
 
1.5%
110 27037
 
1.4%
86 26910
 
1.4%
98 25580
 
1.3%
2 24008
 
1.3%
134 20667
 
1.1%
25 20637
 
1.1%
172 19213
 
1.0%
21 19115
 
1.0%
4 19025
 
1.0%
Other values (164) 1682470
87.9%
ValueCountFrequency (%)
0 3057
 
0.2%
1 29394
1.5%
2 24008
1.3%
3 18345
1.0%
4 19025
1.0%
ValueCountFrequency (%)
373 13950
0.7%
290 10888
0.6%
264 10335
0.5%
262 9775
0.5%
257 9655
0.5%

with_points
Real number (ℝ)

Zeros 

Distinct151
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.96455067
Minimum0
Maximum365
Zeros84261
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:32.294829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q117
median53
Q3106
95-th percentile198
Maximum365
Range365
Interquartile range (IQR)89

Descriptive statistics

Standard deviation66.85675314
Coefficient of variation (CV)0.9421147954
Kurtosis2.271514216
Mean70.96455067
Median Absolute Deviation (MAD)42
Skewness1.352209776
Sum135830124
Variance4469.82544
MonotonicityNot monotonic
2025-06-14T14:45:32.520006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 84261
 
4.4%
1 54409
 
2.8%
2 36948
 
1.9%
3 32031
 
1.7%
4 31592
 
1.7%
96 28771
 
1.5%
10 28128
 
1.5%
6 27993
 
1.5%
5 25031
 
1.3%
19 21802
 
1.1%
Other values (141) 1543090
80.6%
ValueCountFrequency (%)
0 84261
4.4%
1 54409
2.8%
2 36948
1.9%
3 32031
 
1.7%
4 31592
 
1.7%
ValueCountFrequency (%)
365 13950
0.7%
285 10888
0.6%
258 9775
0.5%
255 9655
0.5%
236 9623
0.5%

podiums
Real number (ℝ)

Zeros 

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.99721482
Minimum0
Maximum178
Zeros644683
Zeros (%)33.7%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:32.736764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q316
95-th percentile61
Maximum178
Range178
Interquartile range (IQR)16

Descriptive statistics

Standard deviation26.15523446
Coefficient of variation (CV)1.868602776
Kurtosis15.50558802
Mean13.99721482
Median Absolute Deviation (MAD)4
Skewness3.586461885
Sum26791453
Variance684.0962899
MonotonicityNot monotonic
2025-06-14T14:45:32.948365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 644683
33.7%
1 151482
 
7.9%
2 91274
 
4.8%
3 60535
 
3.2%
9 58725
 
3.1%
4 51215
 
2.7%
5 50025
 
2.6%
13 49382
 
2.6%
15 45950
 
2.4%
10 39007
 
2.0%
Other values (52) 671778
35.1%
ValueCountFrequency (%)
0 644683
33.7%
1 151482
 
7.9%
2 91274
 
4.8%
3 60535
 
3.2%
4 51215
 
2.7%
ValueCountFrequency (%)
178 13950
0.7%
152 6563
0.3%
121 7495
0.4%
119 9623
0.5%
111 4963
 
0.3%

wins
Real number (ℝ)

Zeros 

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.374099295
Minimum0
Maximum118
Zeros851283
Zeros (%)44.5%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:33.155386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile33
Maximum118
Range118
Interquartile range (IQR)7

Descriptive statistics

Standard deviation16.41622401
Coefficient of variation (CV)2.226200564
Kurtosis20.84519708
Mean7.374099295
Median Absolute Deviation (MAD)1
Skewness4.227882286
Sum14114439
Variance269.4924108
MonotonicityNot monotonic
2025-06-14T14:45:33.734612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 851283
44.5%
1 149385
 
7.8%
3 100747
 
5.3%
2 98849
 
5.2%
5 98314
 
5.1%
9 56584
 
3.0%
6 54390
 
2.8%
4 51723
 
2.7%
13 39028
 
2.0%
7 38256
 
2.0%
Other values (34) 375497
19.6%
ValueCountFrequency (%)
0 851283
44.5%
1 149385
 
7.8%
2 98849
 
5.2%
3 100747
 
5.3%
4 51723
 
2.7%
ValueCountFrequency (%)
118 6563
0.3%
111 13950
0.7%
85 7495
0.4%
79 4963
 
0.3%
70 4425
 
0.2%

min_year
Real number (ℝ)

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1993.046939
Minimum1949
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:33.962847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1949
5-th percentile1961
Q11983
median1996
Q32006
95-th percentile2015
Maximum2021
Range72
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.83260582
Coefficient of variation (CV)0.008445664524
Kurtosis-0.3426556504
Mean1993.046939
Median Absolute Deviation (MAD)12
Skewness-0.6368855189
Sum3814803451
Variance283.3366188
MonotonicityNot monotonic
2025-06-14T14:45:34.196086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2002 96703
 
5.1%
2005 62158
 
3.2%
1998 53910
 
2.8%
1996 52473
 
2.7%
1993 51104
 
2.7%
2013 50705
 
2.6%
2012 50689
 
2.6%
2000 48346
 
2.5%
2010 47727
 
2.5%
2008 47138
 
2.5%
Other values (63) 1353103
70.7%
ValueCountFrequency (%)
1949 17022
0.9%
1950 6890
0.4%
1951 5836
 
0.3%
1952 7517
0.4%
1953 4397
 
0.2%
ValueCountFrequency (%)
2021 3549
 
0.2%
2020 3531
 
0.2%
2019 10637
0.6%
2018 18084
0.9%
2017 25029
1.3%

max_year
Real number (ℝ)

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.509057
Minimum1949
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:34.427512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1949
5-th percentile1969
Q11991
median2007
Q32019
95-th percentile2021
Maximum2021
Range72
Interquartile range (IQR)28

Descriptive statistics

Standard deviation17.4797634
Coefficient of variation (CV)0.00872893101
Kurtosis-0.2717869157
Mean2002.509057
Median Absolute Deviation (MAD)14
Skewness-0.7779007779
Sum3832914475
Variance305.5421286
MonotonicityNot monotonic
2025-06-14T14:45:34.654245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2021 377638
 
19.7%
2019 68147
 
3.6%
2020 61027
 
3.2%
2016 52328
 
2.7%
1989 52013
 
2.7%
2018 50085
 
2.6%
2011 43726
 
2.3%
2015 43662
 
2.3%
2012 41575
 
2.2%
1993 40772
 
2.1%
Other values (63) 1083083
56.6%
ValueCountFrequency (%)
1949 688
 
< 0.1%
1950 1628
0.1%
1951 2754
0.1%
1952 2039
0.1%
1953 1652
0.1%
ValueCountFrequency (%)
2021 377638
19.7%
2020 61027
 
3.2%
2019 68147
 
3.6%
2018 50085
 
2.6%
2017 38533
 
2.0%

years_active
Real number (ℝ)

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.377671291
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 MiB
2025-06-14T14:45:34.855589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q312
95-th percentile19
Maximum26
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.144729157
Coefficient of variation (CV)0.548614789
Kurtosis-0.1354508027
Mean9.377671291
Median Absolute Deviation (MAD)4
Skewness0.518302257
Sum17949388
Variance26.46823809
MonotonicityNot monotonic
2025-06-14T14:45:35.052255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8 166808
 
8.7%
10 156151
 
8.2%
11 136733
 
7.1%
7 131530
 
6.9%
6 127323
 
6.7%
9 122607
 
6.4%
5 113780
 
5.9%
4 112170
 
5.9%
12 111519
 
5.8%
3 107833
 
5.6%
Other values (13) 627602
32.8%
ValueCountFrequency (%)
1 65672
3.4%
2 85081
4.4%
3 107833
5.6%
4 112170
5.9%
5 113780
5.9%
ValueCountFrequency (%)
26 13950
 
0.7%
22 9655
 
0.5%
21 10112
 
0.5%
20 46454
2.4%
19 19716
1.0%